STARWEST 2026 - Testing AI in Production

As organizations increasingly deploy AI and machine learning systems into production, testing practices built for static, rule-based software are no longer sufficient. Unlike traditional applications, AI systems learn from data, change behavior over time, and are sensitive to data drift, bias, and feedback loops, making defects harder to detect with conventional test cases. This session presents a practical, experience-driven approach to testing AI systems across the full lifecycle, from model development to live deployment. Drawing on real-world implementations and applied research, the...